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Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research
Kacper Maciejewski1, Patrycja Czerwinska1,2,3
1Undergraduate Research Group "Biobase", Poznan University of Medical Sciences, 61-701 Poznan, Poland.
Cancers
|September 14, 2024
Summary
Spatial transcriptomics (ST) is revolutionizing cancer research by mapping gene expression in tissues. This review highlights leading methods like Visium and data analysis tools, while urging better data sharing for reproducible neoplasm studies.
Area of Science:
- Genomics and Molecular Biology
- Cancer Research
- Bioinformatics
Background:
- Spatial transcriptomics (ST) integrates gene expression data with tissue morphology.
- Advances in ST technology have significantly increased its application in cancer research.
- Understanding gene expression in spatial context is crucial for deciphering complex biological systems.
Purpose of the Study:
- To review current challenges and practical applications of spatial transcriptomics in neoplasm research.
- To summarize existing methods, trends, and data analysis techniques for ST in cancer studies.
- To identify areas for improvement in reproducibility and reliability of ST research.
Main Methods:
- Scoping review of 41 articles published by the end of 2023.
- Analysis of public data repositories.
- Synthesis of common ST platforms, data analysis methods, and integrated data types.
Main Results:
- Cancer biology is a primary focus for ST research, with a growing number of annual publications.
- Visium (10x Genomics) is the dominant ST platform; SCTransform (Seurat) is preferred for data normalization.
- Common applications include tumor microenvironment characterization and cell interaction analysis.
- Nearly half of the reviewed studies lacked comprehensive data processing protocols, impacting reproducibility.
Conclusions:
- Increased transparency in sharing analysis methods is crucial for enhancing ST study reproducibility.
- Careful adaptation of single-cell analysis techniques is recommended for ST data.
- Future ST research in cancer should prioritize robust data processing and transparent reporting to improve reliability.

